Bibliographic record
Abstract
Prediction of building performance is inherently a multi-domain problem. This is particularly true of many modern buildings where the aim is to provide energy efficient operation and high levels of comfort (thermal, visual, acoustic) and indoor air quality. Such buildings may employ sophisticated environmental control systems, and/or energy efficient design features such as natural daylighting, natural ventilation and building-integrated renewables. The ESP-r system allows the analysis of coupled, inter-domain processes, e.g, detailed air flow and dynamic building temperature variation. The program has the capability to model, in an integrated manner, the following domains to variable levels of resolution: thermal, lighting, ventilation (network air flow and CFD), moisture, HVAC, electrical power flow (including renewable energy sources). All of these domains can be subjected to user-defined control action. The modeller can select, based on the particular design, which domains to include in the analysis. This paper discusses the importance of multi-domain modelling and illustrates this with an example of an application where it is important to model interactions between different domains: the detailed modelling of an HVAC system, coupled with the building it serves. The model highlighted is one developed in support of HOT3000 developments at Natural Resources, Canada (NRCan).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".